T cell‐inflamed gene expression profile is associated with favorable disease‐specific survival in non‐hypermutated microsatellite‐stable colorectal cancer patients
Bibliographic record
Abstract
BACKGROUND: The anti-tumor immune response plays a key role in colorectal cancer (CRC) progression and survival. The T cell-inflamed gene expression profile (GEP) is a biomarker predicting response to checkpoint inhibitor immunotherapy across immunogenic cancer types, but the prognostic value in CRC is unknown. We evaluated associations with disease-specific survival, somatic mutations, and examined its differentially expressed genes and pathways among 84 sporadic CRC patients from the Seattle Colon Cancer Family Registry. METHODS: Gene expression profiling was performed using Nanostring's nCounter PanCancer IO 360 panel. Somatic mutations were identified by a targeted DNA sequencing panel. RESULTS: The T cell-inflamed GEP was positively associated with tumor mutation burden and microsatellite instability high (MSI-H). Higher T cell-inflamed GEP had favorable CRC-specific survival (hazard ratio [HR] per standard deviation unit = 0.50, p = 0.004) regardless of hypermutation or MSI status. Analysis of recurrently mutated genes having at least 10 mutation carriers, suggested that the T cell-inflamed GEP is positively associated with RYR1, and negatively associated with APC. However, these associations were attenuated after adjusting for hypermutation or MSI status. We also found that expression of genes RPL23, EPCAM, AREG and ITGA6, and the Wnt signaling pathway was negatively associated with the T cell-inflamed GEP, which might indicate immune-inhibitory mechanisms. CONCLUSIONS: Our results show that the T cell-inflamed GEP is a prognostic biomarker in non-hypermutated microsatellite-stable CRC. This also suggests that patient stratification for immunotherapy within this CRC subgroup should be explored further. Moreover, reported immune-inhibitory gene expression signals may suggest targets for therapeutic combination with immunotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".